| Title |
Machine Learning For the Future Integration of the Circular Economy in Waste Transportation and Treatment Supply Chain |
| ID_Doc |
1838 |
| Authors |
Hala, H; Anass, C; Youssef, B |
| Title |
Machine Learning For the Future Integration of the Circular Economy in Waste Transportation and Treatment Supply Chain |
| Year |
2022 |
| Published |
Ifac Papersonline, 55, 10 |
| DOI |
10.1016/j.ifacol.2022.09.366 |
| Abstract |
Machine-learning technologies are key enablers to enhance the circularity in smart supply chain distributions. The digital and sustainable supply chain is a tangible representation of circular economy future growth that takes environmental challenges, by reducing materials consumption for manufacturing and reducing transportation emissions. Yet, machine learning, circular economy and digital supply chain are novel disciplines. There is limited research to fully acknowledge the promise of circular approaches in the context for waste treatment and transportation. To fill in this gap, this study investigate the integration of circularity using machine-learning techniques for waste treatment supply chain Resulting in a circular economy framework that integrate several tools and concepts, which will assists manufacturers in implementing circular solutions for waste and leachate treatment and transportation. The aim of the study is to present the idea of the approachal application of the opportunities offered by digital technologies and the circularity for waste processing organizations in term of their smart supply chain management. The proposed framework may be handy for practitioners to develop coordinating operations throughout waste treatment supply chain, with the circular economy concepts and digital technology opportunities. Copyright (C) 2022 The Authors. |
| Author Keywords |
Machine learning; circular economy; smart supply chain; waste treatment; transportation |
| Index Keywords |
Index Keywords |
| Document Type |
Other |
| Open Access |
Open Access |
| Source |
Conference Proceedings Citation Index - Science (CPCI-S) |
| EID |
WOS:000881681700009 |
| WoS Category |
Automation & Control Systems |
| Research Area |
Automation & Control Systems |
| PDF |
https://doi.org/10.1016/j.ifacol.2022.09.366
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